Decision Tree - I¶

In [1]:
import pandas as pd
import numpy as np
from sklearn.tree import DecisionTreeClassifier
from sklearn import tree
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.metrics import accuracy_score, confusion_matrix, roc_auc_score, roc_curve
In [2]:
df= pd.read_csv(r"E:\ML by Sudh\Sourse File\winequality_red.csv")
df.head()
Out[2]:
fixed acidity volatile acidity citric acid residual sugar chlorides free sulfur dioxide total sulfur dioxide density pH sulphates alcohol quality
0 7.4 0.70 0.00 1.9 0.076 11.0 34.0 0.9978 3.51 0.56 9.4 5
1 7.8 0.88 0.00 2.6 0.098 25.0 67.0 0.9968 3.20 0.68 9.8 5
2 7.8 0.76 0.04 2.3 0.092 15.0 54.0 0.9970 3.26 0.65 9.8 5
3 11.2 0.28 0.56 1.9 0.075 17.0 60.0 0.9980 3.16 0.58 9.8 6
4 7.4 0.70 0.00 1.9 0.076 11.0 34.0 0.9978 3.51 0.56 9.4 5
In [3]:
from pandas_profiling import ProfileReport
In [4]:
ProfileReport(df)

Summarize dataset:   0%|          | 0/5 [00:00<?, ?it/s]
Generate report structure:   0%|          | 0/1 [00:00<?, ?it/s]
Render HTML:   0%|          | 0/1 [00:00<?, ?it/s]
Out[4]:
























In [5]:



     


df.describe().T



     






















    
    
Out[5]:











  
    

      
      count
      mean
      std
      min
      25%
      50%
      75%
      max
    

  
  
    

      fixed acidity
      1599.0
      8.319637
      1.741096
      4.60000
      7.1000
      7.90000
      9.200000
      15.90000
    

    

      volatile acidity
      1599.0
      0.527821
      0.179060
      0.12000
      0.3900
      0.52000
      0.640000
      1.58000
    

    

      citric acid
      1599.0
      0.270976
      0.194801
      0.00000
      0.0900
      0.26000
      0.420000
      1.00000
    

    

      residual sugar
      1599.0
      2.538806
      1.409928
      0.90000
      1.9000
      2.20000
      2.600000
      15.50000
    

    

      chlorides
      1599.0
      0.087467
      0.047065
      0.01200
      0.0700
      0.07900
      0.090000
      0.61100
    

    

      free sulfur dioxide
      1599.0
      15.874922
      10.460157
      1.00000
      7.0000
      14.00000
      21.000000
      72.00000
    

    

      total sulfur dioxide
      1599.0
      46.467792
      32.895324
      6.00000
      22.0000
      38.00000
      62.000000
      289.00000
    

    

      density
      1599.0
      0.996747
      0.001887
      0.99007
      0.9956
      0.99675
      0.997835
      1.00369
    

    

      pH
      1599.0
      3.311113
      0.154386
      2.74000
      3.2100
      3.31000
      3.400000
      4.01000
    

    

      sulphates
      1599.0
      0.658149
      0.169507
      0.33000
      0.5500
      0.62000
      0.730000
      2.00000
    

    

      alcohol
      1599.0
      10.422983
      1.065668
      8.40000
      9.5000
      10.20000
      11.100000
      14.90000
    

    

      quality
      1599.0
      5.636023
      0.807569
      3.00000
      5.0000
      6.00000
      6.000000
      8.00000
    

  



























In [6]:



     


X= df.drop(columns= 'quality')
X.head()



     






















    
    
Out[6]:











  
    

      
      fixed acidity
      volatile acidity
      citric acid
      residual sugar
      chlorides
      free sulfur dioxide
      total sulfur dioxide
      density
      pH
      sulphates
      alcohol
    

  
  
    

      0
      7.4
      0.70
      0.00
      1.9
      0.076
      11.0
      34.0
      0.9978
      3.51
      0.56
      9.4
    

    

      1
      7.8
      0.88
      0.00
      2.6
      0.098
      25.0
      67.0
      0.9968
      3.20
      0.68
      9.8
    

    

      2
      7.8
      0.76
      0.04
      2.3
      0.092
      15.0
      54.0
      0.9970
      3.26
      0.65
      9.8
    

    

      3
      11.2
      0.28
      0.56
      1.9
      0.075
      17.0
      60.0
      0.9980
      3.16
      0.58
      9.8
    

    

      4
      7.4
      0.70
      0.00
      1.9
      0.076
      11.0
      34.0
      0.9978
      3.51
      0.56
      9.4
    

  



























In [7]:



     


y= df['quality']
y.head()



     






















    
    
Out[7]:








0    5
1    5
2    5
3    6
4    5
Name: quality, dtype: int64
























In [8]:



     


x_train , x_test, y_train,y_test = train_test_split(X,y,test_size = .25,random_state = 30)



     



















In [9]:



     


x_train



     






















    
    
Out[9]:











  
    

      
      fixed acidity
      volatile acidity
      citric acid
      residual sugar
      chlorides
      free sulfur dioxide
      total sulfur dioxide
      density
      pH
      sulphates
      alcohol
    

  
  
    

      1178
      5.6
      0.915
      0.00
      2.1
      0.041
      17.0
      78.0
      0.99346
      3.68
      0.73
      11.4
    

    

      1189
      8.8
      0.955
      0.05
      1.8
      0.075
      5.0
      19.0
      0.99616
      3.30
      0.44
      9.6
    

    

      161
      7.6
      0.680
      0.02
      1.3
      0.072
      9.0
      20.0
      0.99650
      3.17
      1.08
      9.2
    

    

      109
      8.1
      0.785
      0.52
      2.0
      0.122
      37.0
      153.0
      0.99690
      3.21
      0.69
      9.3
    

    

      961
      7.1
      0.560
      0.14
      1.6
      0.078
      7.0
      18.0
      0.99592
      3.27
      0.62
      9.3
    

    

      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
    

    

      919
      8.4
      0.620
      0.12
      1.8
      0.072
      38.0
      46.0
      0.99504
      3.38
      0.89
      11.8
    

    

      1164
      9.0
      0.785
      0.24
      1.7
      0.078
      10.0
      21.0
      0.99692
      3.29
      0.67
      10.0
    

    

      500
      7.8
      0.520
      0.25
      1.9
      0.081
      14.0
      38.0
      0.99840
      3.43
      0.65
      9.0
    

    

      941
      9.9
      0.270
      0.49
      5.0
      0.082
      9.0
      17.0
      0.99484
      3.19
      0.52
      12.5
    

    

      421
      6.6
      0.840
      0.03
      2.3
      0.059
      32.0
      48.0
      0.99520
      3.52
      0.56
      12.3
    

  



1199 rows × 11 columns


























In [10]:



     


x_test



     






















    
    
Out[10]:











  
    

      
      fixed acidity
      volatile acidity
      citric acid
      residual sugar
      chlorides
      free sulfur dioxide
      total sulfur dioxide
      density
      pH
      sulphates
      alcohol
    

  
  
    

      1147
      10.0
      0.410
      0.45
      6.20
      0.071
      6.0
      14.0
      0.99702
      3.21
      0.49
      11.8
    

    

      659
      7.1
      0.840
      0.02
      4.40
      0.096
      5.0
      13.0
      0.99700
      3.41
      0.57
      11.0
    

    

      871
      6.9
      0.560
      0.03
      1.50
      0.086
      36.0
      46.0
      0.99522
      3.53
      0.57
      10.6
    

    

      1333
      9.1
      0.775
      0.22
      2.20
      0.079
      12.0
      48.0
      0.99760
      3.18
      0.51
      9.6
    

    

      1411
      6.4
      0.470
      0.40
      2.40
      0.071
      8.0
      19.0
      0.99630
      3.56
      0.73
      10.6
    

    

      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
    

    

      721
      8.9
      0.480
      0.24
      2.85
      0.094
      35.0
      106.0
      0.99820
      3.10
      0.53
      9.2
    

    

      490
      9.3
      0.775
      0.27
      2.80
      0.078
      24.0
      56.0
      0.99840
      3.31
      0.67
      10.6
    

    

      575
      12.3
      0.270
      0.49
      3.10
      0.079
      28.0
      46.0
      0.99930
      3.20
      0.80
      10.2
    

    

      1051
      8.5
      0.460
      0.59
      1.40
      0.414
      16.0
      45.0
      0.99702
      3.03
      1.34
      9.2
    

    

      1212
      6.8
      0.650
      0.02
      2.10
      0.078
      8.0
      15.0
      0.99498
      3.35
      0.62
      10.4
    

  



400 rows × 11 columns


























In [11]:



     


dt_model = DecisionTreeClassifier()



     



















In [12]:



     


dt_model.fit(x_train, y_train)



     






















    
    
Out[12]:








DecisionTreeClassifier()
























In [13]:



     


dt_model.predict(x_test)



     






















    
    
Out[13]:








array([6, 3, 6, 6, 6, 5, 6, 6, 5, 3, 6, 6, 6, 5, 7, 4, 5, 5, 7, 6, 5, 7,
       5, 5, 6, 5, 8, 6, 5, 5, 5, 6, 6, 6, 6, 7, 6, 6, 5, 6, 6, 6, 7, 5,
       6, 6, 6, 6, 5, 6, 6, 8, 5, 5, 5, 5, 6, 5, 5, 6, 5, 6, 7, 5, 7, 5,
       5, 6, 5, 4, 6, 5, 5, 5, 6, 6, 7, 5, 6, 5, 6, 7, 6, 6, 5, 5, 5, 5,
       5, 6, 5, 5, 6, 4, 6, 6, 4, 6, 5, 4, 5, 5, 5, 6, 6, 5, 6, 5, 6, 5,
       5, 5, 5, 7, 7, 5, 6, 6, 5, 6, 6, 7, 7, 6, 7, 5, 6, 5, 6, 6, 6, 5,
       6, 5, 6, 6, 5, 3, 7, 7, 5, 5, 6, 6, 5, 5, 5, 4, 6, 7, 5, 5, 5, 6,
       5, 6, 6, 6, 5, 5, 5, 6, 6, 5, 6, 5, 6, 5, 6, 6, 6, 6, 6, 6, 5, 6,
       5, 5, 6, 6, 5, 6, 7, 5, 6, 5, 6, 6, 4, 7, 5, 5, 5, 7, 6, 5, 7, 6,
       5, 8, 7, 5, 5, 5, 6, 7, 6, 6, 6, 6, 6, 5, 7, 5, 6, 5, 6, 5, 5, 6,
       6, 6, 7, 6, 5, 6, 5, 5, 7, 5, 5, 5, 7, 7, 5, 5, 5, 7, 6, 6, 5, 6,
       6, 5, 7, 6, 6, 7, 6, 5, 4, 7, 5, 6, 6, 5, 6, 6, 5, 6, 6, 5, 6, 6,
       5, 6, 6, 4, 6, 6, 5, 4, 6, 5, 5, 5, 7, 6, 5, 5, 5, 4, 5, 6, 5, 6,
       6, 5, 6, 6, 5, 6, 5, 7, 5, 6, 5, 6, 7, 6, 5, 6, 6, 5, 6, 5, 6, 5,
       6, 6, 6, 5, 6, 6, 6, 6, 7, 6, 5, 5, 6, 6, 6, 4, 6, 5, 6, 5, 6, 5,
       6, 5, 6, 5, 6, 6, 6, 5, 5, 5, 5, 7, 6, 6, 7, 4, 5, 6, 5, 6, 6, 6,
       5, 5, 6, 5, 7, 5, 6, 6, 5, 7, 5, 5, 7, 5, 7, 7, 6, 6, 5, 5, 5, 6,
       6, 6, 6, 6, 5, 5, 5, 6, 5, 6, 4, 5, 6, 5, 5, 6, 7, 6, 5, 6, 7, 6,
       6, 6, 6, 6], dtype=int64)
























In [14]:



     


dt_model.score(x_test, y_test)



     






















    
    
Out[14]:








0.625
























In [15]:



     


dt_model.score(x_train, y_train)



     






















    
    
Out[15]:








1.0
























In [16]:



     


dt_en= DecisionTreeClassifier(criterion="entropy")



     



















In [17]:



     


dt_en.fit(x_train, y_train)



     






















    
    
Out[17]:








DecisionTreeClassifier(criterion='entropy')
























In [18]:



     


dt_en.score(x_test, y_test)



     






















    
    
Out[18]:








0.6325
























In [19]:



     


outfile= open("dt_en_meta.dot", "w")
tree.export_graphviz(dt_en, out_file= outfile, feature_names= X.columns)



     



















In [20]:



     


dir()



     






















    
    
Out[20]:








['DecisionTreeClassifier',
 'GridSearchCV',
 'In',
 'Out',
 'ProfileReport',
 'X',
 '_',
 '_10',
 '_12',
 '_13',
 '_14',
 '_15',
 '_17',
 '_18',
 '_2',
 '_4',
 '_5',
 '_6',
 '_7',
 '_9',
 '__',
 '___',
 '__builtin__',
 '__builtins__',
 '__doc__',
 '__loader__',
 '__name__',
 '__package__',
 '__spec__',
 '__vsc_ipynb_file__',
 '_dh',
 '_i',
 '_i1',
 '_i10',
 '_i11',
 '_i12',
 '_i13',
 '_i14',
 '_i15',
 '_i16',
 '_i17',
 '_i18',
 '_i19',
 '_i2',
 '_i20',
 '_i3',
 '_i4',
 '_i5',
 '_i6',
 '_i7',
 '_i8',
 '_i9',
 '_ih',
 '_ii',
 '_iii',
 '_oh',
 'accuracy_score',
 'confusion_matrix',
 'df',
 'dt_en',
 'dt_model',
 'exit',
 'get_ipython',
 'np',
 'os',
 'outfile',
 'pd',
 'quit',
 'roc_auc_score',
 'roc_curve',
 'sys',
 'train_test_split',
 'tree',
 'x_test',
 'x_train',
 'y',
 'y_test',
 'y_train']




























Decision Tree Part - II¶


















In [21]:



     


import pandas as pd
import numpy as np
from sklearn.tree import DecisionTreeClassifier
from sklearn import tree
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.metrics import accuracy_score, confusion_matrix, roc_auc_score, roc_curve



     



















In [22]:



     


df.head()



     






















    
    
Out[22]:











  
    

      
      fixed acidity
      volatile acidity
      citric acid
      residual sugar
      chlorides
      free sulfur dioxide
      total sulfur dioxide
      density
      pH
      sulphates
      alcohol
      quality
    

  
  
    

      0
      7.4
      0.70
      0.00
      1.9
      0.076
      11.0
      34.0
      0.9978
      3.51
      0.56
      9.4
      5
    

    

      1
      7.8
      0.88
      0.00
      2.6
      0.098
      25.0
      67.0
      0.9968
      3.20
      0.68
      9.8
      5
    

    

      2
      7.8
      0.76
      0.04
      2.3
      0.092
      15.0
      54.0
      0.9970
      3.26
      0.65
      9.8
      5
    

    

      3
      11.2
      0.28
      0.56
      1.9
      0.075
      17.0
      60.0
      0.9980
      3.16
      0.58
      9.8
      6
    

    

      4
      7.4
      0.70
      0.00
      1.9
      0.076
      11.0
      34.0
      0.9978
      3.51
      0.56
      9.4
      5
    

  



























In [23]:



     


set(df.quality)



     






















    
    
Out[23]:








{3, 4, 5, 6, 7, 8}
























In [24]:



     


x= df.drop(columns= 'quality')
x



     






















    
    
Out[24]:











  
    

      
      fixed acidity
      volatile acidity
      citric acid
      residual sugar
      chlorides
      free sulfur dioxide
      total sulfur dioxide
      density
      pH
      sulphates
      alcohol
    

  
  
    

      0
      7.4
      0.700
      0.00
      1.9
      0.076
      11.0
      34.0
      0.99780
      3.51
      0.56
      9.4
    

    

      1
      7.8
      0.880
      0.00
      2.6
      0.098
      25.0
      67.0
      0.99680
      3.20
      0.68
      9.8
    

    

      2
      7.8
      0.760
      0.04
      2.3
      0.092
      15.0
      54.0
      0.99700
      3.26
      0.65
      9.8
    

    

      3
      11.2
      0.280
      0.56
      1.9
      0.075
      17.0
      60.0
      0.99800
      3.16
      0.58
      9.8
    

    

      4
      7.4
      0.700
      0.00
      1.9
      0.076
      11.0
      34.0
      0.99780
      3.51
      0.56
      9.4
    

    

      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
    

    

      1594
      6.2
      0.600
      0.08
      2.0
      0.090
      32.0
      44.0
      0.99490
      3.45
      0.58
      10.5
    

    

      1595
      5.9
      0.550
      0.10
      2.2
      0.062
      39.0
      51.0
      0.99512
      3.52
      0.76
      11.2
    

    

      1596
      6.3
      0.510
      0.13
      2.3
      0.076
      29.0
      40.0
      0.99574
      3.42
      0.75
      11.0
    

    

      1597
      5.9
      0.645
      0.12
      2.0
      0.075
      32.0
      44.0
      0.99547
      3.57
      0.71
      10.2
    

    

      1598
      6.0
      0.310
      0.47
      3.6
      0.067
      18.0
      42.0
      0.99549
      3.39
      0.66
      11.0
    

  



1599 rows × 11 columns


























In [25]:



     


y= df.quality
y



     






















    
    
Out[25]:








0       5
1       5
2       5
3       6
4       5
       ..
1594    5
1595    6
1596    6
1597    5
1598    6
Name: quality, Length: 1599, dtype: int64
























In [26]:



     


x_train, x_test, y_train, y_test= train_test_split(x, y, test_size= 0.25, random_state= 30)



     



















In [27]:



     


x_train



     






















    
    
Out[27]:











  
    

      
      fixed acidity
      volatile acidity
      citric acid
      residual sugar
      chlorides
      free sulfur dioxide
      total sulfur dioxide
      density
      pH
      sulphates
      alcohol
    

  
  
    

      1178
      5.6
      0.915
      0.00
      2.1
      0.041
      17.0
      78.0
      0.99346
      3.68
      0.73
      11.4
    

    

      1189
      8.8
      0.955
      0.05
      1.8
      0.075
      5.0
      19.0
      0.99616
      3.30
      0.44
      9.6
    

    

      161
      7.6
      0.680
      0.02
      1.3
      0.072
      9.0
      20.0
      0.99650
      3.17
      1.08
      9.2
    

    

      109
      8.1
      0.785
      0.52
      2.0
      0.122
      37.0
      153.0
      0.99690
      3.21
      0.69
      9.3
    

    

      961
      7.1
      0.560
      0.14
      1.6
      0.078
      7.0
      18.0
      0.99592
      3.27
      0.62
      9.3
    

    

      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
    

    

      919
      8.4
      0.620
      0.12
      1.8
      0.072
      38.0
      46.0
      0.99504
      3.38
      0.89
      11.8
    

    

      1164
      9.0
      0.785
      0.24
      1.7
      0.078
      10.0
      21.0
      0.99692
      3.29
      0.67
      10.0
    

    

      500
      7.8
      0.520
      0.25
      1.9
      0.081
      14.0
      38.0
      0.99840
      3.43
      0.65
      9.0
    

    

      941
      9.9
      0.270
      0.49
      5.0
      0.082
      9.0
      17.0
      0.99484
      3.19
      0.52
      12.5
    

    

      421
      6.6
      0.840
      0.03
      2.3
      0.059
      32.0
      48.0
      0.99520
      3.52
      0.56
      12.3
    

  



1199 rows × 11 columns


























In [28]:



     


x_test



     






















    
    
Out[28]:











  
    

      
      fixed acidity
      volatile acidity
      citric acid
      residual sugar
      chlorides
      free sulfur dioxide
      total sulfur dioxide
      density
      pH
      sulphates
      alcohol
    

  
  
    

      1147
      10.0
      0.410
      0.45
      6.20
      0.071
      6.0
      14.0
      0.99702
      3.21
      0.49
      11.8
    

    

      659
      7.1
      0.840
      0.02
      4.40
      0.096
      5.0
      13.0
      0.99700
      3.41
      0.57
      11.0
    

    

      871
      6.9
      0.560
      0.03
      1.50
      0.086
      36.0
      46.0
      0.99522
      3.53
      0.57
      10.6
    

    

      1333
      9.1
      0.775
      0.22
      2.20
      0.079
      12.0
      48.0
      0.99760
      3.18
      0.51
      9.6
    

    

      1411
      6.4
      0.470
      0.40
      2.40
      0.071
      8.0
      19.0
      0.99630
      3.56
      0.73
      10.6
    

    

      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
    

    

      721
      8.9
      0.480
      0.24
      2.85
      0.094
      35.0
      106.0
      0.99820
      3.10
      0.53
      9.2
    

    

      490
      9.3
      0.775
      0.27
      2.80
      0.078
      24.0
      56.0
      0.99840
      3.31
      0.67
      10.6
    

    

      575
      12.3
      0.270
      0.49
      3.10
      0.079
      28.0
      46.0
      0.99930
      3.20
      0.80
      10.2
    

    

      1051
      8.5
      0.460
      0.59
      1.40
      0.414
      16.0
      45.0
      0.99702
      3.03
      1.34
      9.2
    

    

      1212
      6.8
      0.650
      0.02
      2.10
      0.078
      8.0
      15.0
      0.99498
      3.35
      0.62
      10.4
    

  



400 rows × 11 columns


























In [29]:



     


dt_model= DecisionTreeClassifier()



     



















In [30]:



     


dt_model.fit(x_train, y_train)



     






















    
    
Out[30]:








DecisionTreeClassifier()
























In [31]:



     


import sklearn
import matplotlib.pyplot as plt
from sklearn import tree
%matplotlib inline



     



















In [32]:



     


sklearn.__version__



     






















    
    
Out[32]:








'1.0.2'
























In [33]:



     


plt.figure(figsize=(20,20))
tree.plot_tree(dt_model, filled=True)



     






















    
    
Out[33]:








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 Text(0.9403239556692242, 0.75, 'X[5] <= 9.5\ngini = 0.465\nsamples = 57\nvalue = [0, 0, 1, 10, 40, 6]'),
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 Text(0.9215686274509803, 0.6388888888888888, 'X[3] <= 2.45\ngini = 0.198\nsamples = 9\nvalue = [0, 0, 0, 0, 8, 1]'),
 Text(0.9181585677749361, 0.5833333333333334, 'gini = 0.0\nsamples = 8\nvalue = [0, 0, 0, 0, 8, 0]'),
 Text(0.9249786871270247, 0.5833333333333334, 'gini = 0.0\nsamples = 1\nvalue = [0, 0, 0, 0, 0, 1]'),
 Text(0.9352088661551577, 0.6388888888888888, 'X[9] <= 0.695\ngini = 0.623\nsamples = 18\nvalue = [0, 0, 0, 9, 5, 4]'),
 Text(0.9317988064791134, 0.5833333333333334, 'gini = 0.0\nsamples = 3\nvalue = [0, 0, 0, 0, 0, 3]'),
 Text(0.9386189258312021, 0.5833333333333334, 'X[3] <= 2.625\ngini = 0.524\nsamples = 15\nvalue = [0, 0, 0, 9, 5, 1]'),
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 Text(0.9522591645353794, 0.4722222222222222, 'gini = 0.0\nsamples = 1\nvalue = [0, 0, 0, 1, 0, 0]'),
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 Text(0.9624893435635123, 0.5277777777777778, 'gini = 0.0\nsamples = 5\nvalue = [0, 0, 0, 0, 5, 0]'),
 Text(0.9846547314578005, 0.75, 'X[8] <= 3.36\ngini = 0.566\nsamples = 25\nvalue = [0, 0, 1, 13, 10, 1]'),
 Text(0.9761295822676896, 0.6944444444444444, 'X[3] <= 2.15\ngini = 0.439\nsamples = 14\nvalue = [0, 0, 1, 3, 10, 0]'),
 Text(0.969309462915601, 0.6388888888888888, 'X[9] <= 0.77\ngini = 0.48\nsamples = 5\nvalue = [0, 0, 0, 3, 2, 0]'),
 Text(0.9658994032395567, 0.5833333333333334, 'gini = 0.0\nsamples = 2\nvalue = [0, 0, 0, 0, 2, 0]'),
 Text(0.9727195225916454, 0.5833333333333334, 'gini = 0.0\nsamples = 3\nvalue = [0, 0, 0, 3, 0, 0]'),
 Text(0.9829497016197783, 0.6388888888888888, 'X[4] <= 0.093\ngini = 0.198\nsamples = 9\nvalue = [0, 0, 1, 0, 8, 0]'),
 Text(0.979539641943734, 0.5833333333333334, 'gini = 0.0\nsamples = 8\nvalue = [0, 0, 0, 0, 8, 0]'),
 Text(0.9863597612958227, 0.5833333333333334, 'gini = 0.0\nsamples = 1\nvalue = [0, 0, 1, 0, 0, 0]'),
 Text(0.9931798806479113, 0.6944444444444444, 'X[8] <= 3.7\ngini = 0.165\nsamples = 11\nvalue = [0, 0, 0, 10, 0, 1]'),
 Text(0.989769820971867, 0.6388888888888888, 'gini = 0.0\nsamples = 10\nvalue = [0, 0, 0, 10, 0, 0]'),
 Text(0.9965899403239556, 0.6388888888888888, 'gini = 0.0\nsamples = 1\nvalue = [0, 0, 0, 0, 0, 1]')]










    
    































In [34]:



     


df1= df.head(30)



     



















In [35]:



     


x1= df1.drop(columns= 'quality')



     



















In [36]:



     


y1= df1.quality



     



















In [37]:



     


dt_model1= DecisionTreeClassifier()
dt_model1.fit(x1, y1)



     






















    
    
Out[37]:








DecisionTreeClassifier()
























In [38]:



     


plt.figure(figsize=(20,20))
tree.plot_tree(dt_model1, filled=True)



     






















    
    
Out[38]:








[Text(0.5909090909090909, 0.9166666666666666, 'X[2] <= 0.42\ngini = 0.471\nsamples = 30\nvalue = [1, 21, 5, 3]'),
 Text(0.36363636363636365, 0.75, 'X[6] <= 22.0\ngini = 0.334\nsamples = 26\nvalue = [1, 21, 2, 2]'),
 Text(0.18181818181818182, 0.5833333333333334, 'X[4] <= 0.076\ngini = 0.625\nsamples = 4\nvalue = [0, 1, 1, 2]'),
 Text(0.09090909090909091, 0.4166666666666667, 'gini = 0.0\nsamples = 2\nvalue = [0, 0, 0, 2]'),
 Text(0.2727272727272727, 0.4166666666666667, 'X[6] <= 13.5\ngini = 0.5\nsamples = 2\nvalue = [0, 1, 1, 0]'),
 Text(0.18181818181818182, 0.25, 'gini = 0.0\nsamples = 1\nvalue = [0, 1, 0, 0]'),
 Text(0.36363636363636365, 0.25, 'gini = 0.0\nsamples = 1\nvalue = [0, 0, 1, 0]'),
 Text(0.5454545454545454, 0.5833333333333334, 'X[10] <= 9.05\ngini = 0.169\nsamples = 22\nvalue = [1, 20, 1, 0]'),
 Text(0.45454545454545453, 0.4166666666666667, 'gini = 0.0\nsamples = 1\nvalue = [1, 0, 0, 0]'),
 Text(0.6363636363636364, 0.4166666666666667, 'X[1] <= 0.415\ngini = 0.091\nsamples = 21\nvalue = [0, 20, 1, 0]'),
 Text(0.5454545454545454, 0.25, 'X[1] <= 0.395\ngini = 0.444\nsamples = 3\nvalue = [0, 2, 1, 0]'),
 Text(0.45454545454545453, 0.08333333333333333, 'gini = 0.0\nsamples = 2\nvalue = [0, 2, 0, 0]'),
 Text(0.6363636363636364, 0.08333333333333333, 'gini = 0.0\nsamples = 1\nvalue = [0, 0, 1, 0]'),
 Text(0.7272727272727273, 0.25, 'gini = 0.0\nsamples = 18\nvalue = [0, 18, 0, 0]'),
 Text(0.8181818181818182, 0.75, 'X[6] <= 81.5\ngini = 0.375\nsamples = 4\nvalue = [0, 0, 3, 1]'),
 Text(0.7272727272727273, 0.5833333333333334, 'gini = 0.0\nsamples = 3\nvalue = [0, 0, 3, 0]'),
 Text(0.9090909090909091, 0.5833333333333334, 'gini = 0.0\nsamples = 1\nvalue = [0, 0, 0, 1]')]










    
    































In [39]:



     


set(y1)



     






















    
    
Out[39]:








{4, 5, 6, 7}
























In [42]:



     


plt.figure(figsize=(20,20))
tree.plot_tree(dt_model1, filled= True, class_names= [str(i) for i in set(y1)], feature_names= x1.columns)
plt.savefig('dt_model_1')



     






















    
    































In [43]:



     


dt_model1.predict(x1)



     






















    
    
Out[43]:








array([5, 5, 5, 6, 5, 5, 5, 7, 7, 5, 5, 5, 5, 5, 5, 5, 7, 5, 4, 6, 6, 5,
       5, 5, 6, 5, 5, 5, 5, 6], dtype=int64)
























In [44]:



     


dt_model1.score(x1, y1)



     






















    
    
Out[44]:








1.0
























In [45]:



     


dt_model1.score(x_test, y_test)



     






















    
    
Out[45]:








0.465
























In [46]:



     


path= dt_model1.cost_complexity_pruning_path(x1, y1)
path # It's the complexity of pruning



     






















    
    
Out[46]:








{'ccp_alphas': array([0.        , 0.03174603, 0.03333333, 0.05      , 0.05      ,
        0.06075036, 0.08216783, 0.13136752]),
 'impurities': array([0.        , 0.06349206, 0.0968254 , 0.1468254 , 0.1968254 ,
        0.25757576, 0.33974359, 0.47111111])}
























In [47]:



     


ccp_alpha= path.ccp_alphas



     



















In [48]:



     


ccp_alpha



     






















    
    
Out[48]:








array([0.        , 0.03174603, 0.03333333, 0.05      , 0.05      ,
       0.06075036, 0.08216783, 0.13136752])
























In [50]:



     


dt_modle2 = []
for ccp in ccp_alpha:
    dt_m  = DecisionTreeClassifier(ccp_alpha=ccp)
    dt_m.fit(x1,y1)
    dt_modle2.append(dt_m)



     



















In [51]:



     


dt_modle2



     






















    
    
Out[51]:








[DecisionTreeClassifier(),
 DecisionTreeClassifier(ccp_alpha=0.03174603174603176),
 DecisionTreeClassifier(ccp_alpha=0.03333333333333333),
 DecisionTreeClassifier(ccp_alpha=0.049999999999999996),
 DecisionTreeClassifier(ccp_alpha=0.05),
 DecisionTreeClassifier(ccp_alpha=0.06075036075036072),
 DecisionTreeClassifier(ccp_alpha=0.08216783216783222),
 DecisionTreeClassifier(ccp_alpha=0.1313675213675214)]
























In [52]:



     


dt_model2 = []
score = []
for i in ccp_alpha:
    dt_m = DecisionTreeClassifier(ccp_alpha = i)
    dt_m.fit(x1,y1)
    dt_model2.append(dt_m)
    score.append(dt_m.score(x_test, y_test))

    
plt.plot(score, 'r-+')
plt.xlabel('iteration')
plt.ylabel('score')



     






















    
    
Out[52]:








Text(0, 0.5, 'score')










    
    































In [53]:



     


train_score= [i.score(x1, y1) for i in dt_modle2]



     



















In [54]:



     


test_score= [i.score(x_test, y_test) for i in dt_modle2]



     



















In [55]:



     


train_score



     






















    
    
Out[55]:








[1.0,
 0.9666666666666667,
 0.9333333333333333,
 0.9,
 0.8666666666666667,
 0.8333333333333334,
 0.8,
 0.7]
























In [56]:



     


test_score



     






















    
    
Out[56]:








[0.46, 0.485, 0.45, 0.4075, 0.445, 0.445, 0.4625, 0.4225]
























In [57]:



     


plt.plot(train_score, 'r-+', label= 'train')
plt.plot(test_score, 'b-', label= 'validation')
plt.xlabel("Training set size")
plt.ylabel("Decision Tree Score")
plt.legend()
plt.show()



     






















    
    































In [58]:



     


fig, ax = plt.subplots()
ax.set_xlabel("alpha")
ax.set_ylabel("accuracy")
ax.set_title("Accuracy vs alpha for training and testing sets")
ax.plot(ccp_alpha, train_score, marker='o', label="train",
        drawstyle="steps-post")
ax.plot(ccp_alpha, test_score, marker='o', label="test",
        drawstyle="steps-post")
ax.legend()
plt.show()



     






















    
    































In [59]:



     


dt_model_ccp = DecisionTreeClassifier(random_state=0 , ccp_alpha=.014)
dt_model_ccp.fit(x1, y1)



     






















    
    
Out[59]:








DecisionTreeClassifier(ccp_alpha=0.014, random_state=0)
























In [60]:



     


plt.figure(figsize=(20, 20))
tree.plot_tree(dt_model_ccp, filled=True)



     






















    
    
Out[60]:








[Text(0.625, 0.9166666666666666, 'X[2] <= 0.42\ngini = 0.471\nsamples = 30\nvalue = [1, 21, 5, 3]'),
 Text(0.4166666666666667, 0.75, 'X[6] <= 22.0\ngini = 0.334\nsamples = 26\nvalue = [1, 21, 2, 2]'),
 Text(0.25, 0.5833333333333334, 'X[5] <= 8.5\ngini = 0.625\nsamples = 4\nvalue = [0, 1, 1, 2]'),
 Text(0.16666666666666666, 0.4166666666666667, 'X[7] <= 0.996\ngini = 0.5\nsamples = 2\nvalue = [0, 1, 1, 0]'),
 Text(0.08333333333333333, 0.25, 'gini = 0.0\nsamples = 1\nvalue = [0, 1, 0, 0]'),
 Text(0.25, 0.25, 'gini = 0.0\nsamples = 1\nvalue = [0, 0, 1, 0]'),
 Text(0.3333333333333333, 0.4166666666666667, 'gini = 0.0\nsamples = 2\nvalue = [0, 0, 0, 2]'),
 Text(0.5833333333333334, 0.5833333333333334, 'X[5] <= 7.5\ngini = 0.169\nsamples = 22\nvalue = [1, 20, 1, 0]'),
 Text(0.5, 0.4166666666666667, 'gini = 0.0\nsamples = 1\nvalue = [1, 0, 0, 0]'),
 Text(0.6666666666666666, 0.4166666666666667, 'X[1] <= 0.415\ngini = 0.091\nsamples = 21\nvalue = [0, 20, 1, 0]'),
 Text(0.5833333333333334, 0.25, 'X[4] <= 0.084\ngini = 0.444\nsamples = 3\nvalue = [0, 2, 1, 0]'),
 Text(0.5, 0.08333333333333333, 'gini = 0.0\nsamples = 2\nvalue = [0, 2, 0, 0]'),
 Text(0.6666666666666666, 0.08333333333333333, 'gini = 0.0\nsamples = 1\nvalue = [0, 0, 1, 0]'),
 Text(0.75, 0.25, 'gini = 0.0\nsamples = 18\nvalue = [0, 18, 0, 0]'),
 Text(0.8333333333333334, 0.75, 'X[6] <= 81.5\ngini = 0.375\nsamples = 4\nvalue = [0, 0, 3, 1]'),
 Text(0.75, 0.5833333333333334, 'gini = 0.0\nsamples = 3\nvalue = [0, 0, 3, 0]'),
 Text(0.9166666666666666, 0.5833333333333334, 'gini = 0.0\nsamples = 1\nvalue = [0, 0, 0, 1]')]










    
    































In [63]:



     


dt_model_ccp.score(x_train, y_train)



     






















    
    
Out[63]:








0.40950792326939117
























In [62]:



     


dt_model_ccp.score(x_test, y_test)



     






















    
    
Out[62]:








0.47
























In [64]:



     


dt_model_ccp.score(x1, y1)



     






















    
    
Out[64]:








1.0
























In [65]:



     


dt_model2



     






















    
    
Out[65]:








[DecisionTreeClassifier(),
 DecisionTreeClassifier(ccp_alpha=0.03174603174603176),
 DecisionTreeClassifier(ccp_alpha=0.03333333333333333),
 DecisionTreeClassifier(ccp_alpha=0.049999999999999996),
 DecisionTreeClassifier(ccp_alpha=0.05),
 DecisionTreeClassifier(ccp_alpha=0.06075036075036072),
 DecisionTreeClassifier(ccp_alpha=0.08216783216783222),
 DecisionTreeClassifier(ccp_alpha=0.1313675213675214)]
























In [67]:



     


grid_pram = {"criterion":['gini','entropy'],
             "splitter":['best','random'],
             "max_depth" : range(2,40,1),
             "min_samples_split":range(2,10 ,1),
             "min_samples_leaf":range(1,10,1),
             'ccp_alpha':np.random.rand(20)
             }



     



















In [68]:



     


grid_ccp = GridSearchCV(estimator= dt_model_ccp,
param_grid= grid_pram,
 cv= 10,
 n_jobs= -1)



     























grid_ccp.fit(x1, y1)
grid_ccp.best_params_




















In [72]:



     


dt_cpp_new = DecisionTreeClassifier(criterion='entropy', 
max_depth= 17,
min_samples_leaf= 1, 
min_samples_split= 3,
 splitter= 'random',
 ccp_alpha= 0.014)



     



















In [75]:



     


dt_cpp_new.fit(x1, y1)



     






















    
    
Out[75]:








DecisionTreeClassifier(ccp_alpha=0.014, criterion='entropy', max_depth=17,
                       min_samples_split=3, splitter='random')
























In [76]:



     


dt_cpp_new.score(x1, y1)



     






















    
    
Out[76]:








1.0
























In [77]:



     


dt_cpp_new.score(x_test, y_test)



     






















    
    
Out[77]:








0.34
























In [78]:



     


pred = dt_cpp_new.predict(x_train)



     



















In [79]:



     


pred



     






















    
    
Out[79]:








array([5, 7, 7, ..., 5, 4, 5], dtype=int64)
























In [80]:



     


confusion_matrix(y_train, pred)



     






















    
    
Out[80]:








array([[  0,   2,   5,   1,   0,   0],
       [  0,   3,  20,   4,   8,   0],
       [  0,  23, 288,  77, 124,   0],
       [  0,  33, 294,  86,  64,   0],
       [  0,  24,  94,  26,   9,   0],
       [  0,   2,  10,   1,   1,   0]], dtype=int64)